<p>This tutorial shows how to build a Deep Learning model in H<sub>2</sub>O for
classification and regression problems.</p>

<p>H<sub>2</sub>O Deep Learning is based on a multi-layer feed-forward
artifical neural network that is trained with stochastic gradient descent using
back-propragation. The network can contain a large number of hidden layers
consisting of neurons with tanh, rectifier and maxout activation functions.
Advanced features such as adaptive learning rate, rate annealing, momentum
training, dropout, L1/L2 regularization, checkpointing and grid search enable
high predictive accuracy. Each compute node trains a copy of the global model
parameters on its local data with multi-threading (asynchronously), and
contributes periodically to the global model via model averaging across the
network.</p>

<p>
Vocabulary:
<ul>
  <li><a href="http://en.wikipedia.org/wiki/Deep_learning">Deep Learning</a></li>
  <li><a href="http://en.wikipedia.org/wiki/Artificial_neural_network">Artifical Neural Network</a></li>
  <li><a href="http://en.wikipedia.org/wiki/Dataset">Dataset</a></li>
  <li><a href="http://en.wikipedia.org/wiki/Statistical_classification">Classification</a></li>
  <li><a href="http://en.wikipedia.org/wiki/Statistical_regression">Regression</a></li>
</ul>
</p>

